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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices

Extremely Lightweight Quantization Robust Real-Time Single-I...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ayazoglu, Mustafa Aselsan Res Ankara Turkey

ISBN: (纸本)9781665448994

Single-Image Super Resolution (SISR) is a classical computer vision problem and it has been studied for over decades. With the recent success of deep learning methods, recent work on SISR focuses solutions with deep learning methodologies and achieves state-of-the-art results. However most of the state-of-the-art SISR methods contain millions of parameters and layers, which limits their practical applications. In this paper, we propose a hardware (Synaptics Dolphin NPU) limitation aware, extremely lightweight quantization robust real-time super resolution network (XLSR). The proposed model's building block is inspired from root modules introduced in [15] for Image classification. We successfully applied root modules to SISR problem, further more to make the model uint8 quantization robust we used Clipped ReLU at the last layer of the network and achieved great balance between reconstruction quality and runtime. Furthermore, although the proposed network contains 30x fewer parameters than VDSR [16] its performance surpasses it on Div2K validation set. The network proved itself by winning Mobile AI 2021 Real-Time Single Image Super Resolution Challenge.

关键词： Performance evaluation Deep learning computer vision Quantization (signal) Image resolution Runtime Computational modeling

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Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets

Towards Good Practices for Efficiently Annotating Large-Scal...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liao, Yuan-Hong Kar, Amlan Fidler, Sanja Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Data is the engine of modern computer vision, which necessitates collecting large-scale datasets. This is expensive, and guaranteeing the quality of the labels is a major challenge. In this paper, we investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. While methods that exploit learnt models for labeling exist, a surprisingly prevalent approach is to query humans for a fixed number of labels per datum and aggregate them, which is expensive. Building on prior work on online joint probabilistic modeling of human annotations and machine-generated beliefs, we propose modifications and best practices aimed at minimizing human labeling effort. Specifically, we make use of advances in self-supervised learning, view annotation as a semi-supervised learning problem, identify and mitigate pitfalls and ablate several key design choices to propose effective guidelines for labeling. Our analysis is done in a more realistic simulation that involves querying human labelers, which uncovers issues with evaluation using existing worker simulation methods. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average, a 2.7x and 6.7x improvement over prior work and manual annotation, respectively.(1)

关键词： computer vision Analytical models Annotations Manuals Semisupervised learning Probabilistic logic pattern recognition

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Towards Open World Object Detection

Towards Open World Object Detection

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9781665445092

Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: 'Open World Object Detection', where a model is tasked to: 1) identify objects that have not been introduced to it as 'unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received. We formulate the problem, introduce a strong evaluation protocol and provide a novel solution, which we call ORE: Open World Object Detector, based on contrastive clustering and energy based unknown identification. Our experimental evaluation and ablation studies analyse the efficacy of ORE in achieving Open World objectives. As an interesting by-product, we find that identifying and characterising unknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-ofthe-art performance, with no extra methodological effort. We hope that our work will attract further research into this newly identified, yet crucial research direction.'

关键词： computer vision Protocols Computational modeling Object detection Detectors Benchmark testing pattern recognition

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Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation

Summarize the Past to Predict the Future: Natural Language D...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Razvan–George Pasca Alexey Gavryushin Muhammad Hamza Yen-Ling Kuo Kaichun Mo Luc Van Gool Otmar Hilliges Xi Wang ETH Zurich Univ. of Zurich Univ. of Virginia NVIDIA KU Leuven INSAIT Sofia

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We study object interaction anticipation in egocentric videos. This task requires an understanding of the spatio-temporal context formed by past actions on objects, coined action context. We propose TransFusion, a multimodal transformer-based architecture for short-term object interaction anticipation. Our method exploits the representational power of language by summarizing the action con-text textually, after leveraging pre-trained vision-language foundation models to extract the action context from past video frames. The summarized action context and the last observed video frame are processed by the multimodal fusion module to forecast the next object interaction. Experiments on the Ego4D next active object interaction dataset show the effectiveness of our multimodal fusion model and highlight the benefits of using the power of foundation models and language-based context summaries in a task where vision may appear to suffice. Our novel approach outperforms all state-of-the-art methods on both versions of the Ego4D dataset. A project video and code are available at https://***/transfusion-proj/.

关键词： computer vision Codes Natural languages computer architecture Transformers pattern recognition Videos

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Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

Boosting Monocular Depth Estimation Models to High-Resolutio...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Miangoleh, S. Mahdi H. Dille, Sebastian Mai, Long Paris, Sylvain Aksoy, Yagiz Simon Fraser Univ Burnaby BC Canada Adobe Res Bangalore Karnataka India

ISBN: (纸本)9781665445092

Neural networks have shown great abilities in estimating depth from a single image. However, the inferred depth maps are well below one-megapixel resolution and often lack fine-grained details, which limits their practicality. Our method builds on our analysis on how the input resolution and the scene structure affects depth estimation performance. We demonstrate that there is a trade-off between a consistent scene structure and the high-frequency details, and merge low- and high-resolution estimations to take advantage of this duality using a simple depth merging network. We present a double estimation method that improves the whole-image depth estimation and a patch selection method that adds local details to the final result. We demonstrate that by merging estimations at different resolutions with changing context, we can generate multi-megapixel depth maps with a high level of detail using a pre-trained model.

关键词： Location awareness Image segmentation computer vision Image resolution Merging Neural networks Estimation

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Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning

Revamping Cross-Modal Recipe Retrieval with Hierarchical Tra...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Salvador, Amaia Gundogdu, Erhan Bazzani, Loris Donoser, Michael Amazon Seattle WA 98109 USA

ISBN: (纸本)9781665445092

Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people's lives, as well as the availability of vast amounts of digital cooking recipes and food images to train machine learning models. In this work, we revisit existing approaches for cross-modal recipe retrieval and propose a simplified end-to-end model based on well established and high performing encoders for text and images. We introduce a hierarchical recipe Transformer which attentively encodes individual recipe components (titles, ingredients and instructions). Further, we propose a self-supervised loss function computed on top of pairs of individual recipe components, which is able to leverage semantic relationships within recipes, and enables training using both image-recipe and recipe-only samples. We conduct a thorough analysis and ablation studies to validate our design choices. As a result, our proposed method achieves state-of-the-art performance in the cross-modal recipe retrieval task on the Recipe1M dataset. We make code and models publicly available(1).

关键词： Training computer vision Codes Computational modeling Semantics Machine learning Transformers

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Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing

Exploiting Spatial Dimensions of Latent in GAN for Real-time...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kim, Hyunsu Choi, Yunjey Kim, Junho Yoo, Sungjoo Uh, Youngjung NAVER AI Lab Seoul South Korea Seoul Natl Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder. We propose StyleMapGAN: the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN. It makes the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs. Experimental results demonstrate that our method significantly outperforms state-of-the-art models in various image manipulation tasks such as local editing and image interpolation. Last but not least, conventional editing methods on GANs are still valid on our StyleMapGAN.

关键词： Interpolation computer vision Codes Computational modeling Modulation Generative adversarial networks Real-time systems

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Student-Teacher Learning from Clean Inputs to Noisy Inputs

Student-Teacher Learning from Clean Inputs to Noisy Inputs

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hong, Guanzhe Mao, Zhiyuan Lin, Xiaojun Chan, Stanley H. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781665445092

Feature-based student-teacher learning, a training method that encourages the student's hidden features to mimic those of the teacher network, is empirically successful in transferring the knowledge from a pre-trained teacher network to the student network. Furthermore, recent empirical results demonstrate that, the teacher's features can boost the student network's generalization even when the student's input sample is corrupted by noise. However, there is a lack of theoretical insights into why and when this method of transferring knowledge can be successful between such heterogeneous tasks. We analyze this method theoretically using deep linear networks, and experimentally using nonlinear networks. We identify three vital factors to the success of the method: (1) whether the student is trained to zero training loss;(2) how knowledgeable the teacher is on the clean-input problem;(3) how the teacher decomposes its knowledge in its hidden features. Lack of proper control in any of the three factors leads to failure of the student-teacher learning method.

关键词： Training Learning systems Knowledge engineering computer vision Systematics Protocols Numerical analysis

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Unsupervised Disentanglement of Linear-Encoded Facial Semantics

Unsupervised Disentanglement of Linear-Encoded Facial Semant...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zheng, Yutong Huang, Yu-Kai Tao, Ran Shen, Zhiqiang Savvides, Marios Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision. The method derives from linear regression and sparse representation learning concepts to make the disentangled latent representations easily interpreted as well. We start by coupling StyleGAN with a stabilized 3D deformable facial reconstruction method to decompose single-view GAN generations into multiple semantics. Latent representations are then extracted to capture interpretable facial semantics. In this work, we make it possible to get rid of labels for disentangling meaningful facial semantics. Also, we demonstrate that the guided extrapolation along the disentangled representations can help with data augmentation, which sheds light on handling unbalanced data. Finally, we provide an analysis of our learned localized facial representations and illustrate that the semantic information is encoded, which surprisingly complies with human intuition. The overall unsupervised design brings more flexibility to representation learning in the wild.

关键词： Couplings Extrapolation computer vision Three-dimensional displays Semantics Linear regression Reconstruction algorithms

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Convolutional Hough Matching Networks

Convolutional Hough Matching Networks

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Min, Juhong Cho, Minsu POSTECH CSE Pohang South Korea POSTECH GSAI Pohang South Korea

ISBN: (纸本)9781665445092

Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM). The method distributes similarities of candidate matches over a geometric transformation space and evaluate them in a convolutional manner. We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters. To validate the effect, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling. Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.

关键词： Visualization computer vision Semantics Neural networks Transforms Benchmark testing Robustness

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